1. Field of the Invention
The present invention relates to the assessment and management of financial assets and, more particularly, to systems, processes and products involving investment vehicles, particularly, mutual funds and the like.
2. The Prior Art
As of the year 2000, the mutual fund industry is one of the fastest growing financial industries in the United States. Investment in mutual funds often is preferred over investment in individual stocks and bonds' because of four critically desirable characteristics: (1) broad diversification; (2) professional management; (3) liquidity; and (4) convenience.
A mutual fund is a financial intermediary, which sells shares to the public and invests the proceeds in financial assets including (1) stocks, (2) bonds and (3) cash financial securities. Obviously, a fund's profit and loss statement reflects interest, dividends and capital gains on one hand, and costs, expenses and capital losses on the other hand. Ordinarily, highly skilled and highly paid management and research services are among a mutual fund's largest expenses.
Obtaining higher rates of return is a preeminent objective of mutual fund management and research. According to “portfolio theory”, as developed by economists, every investment may be characterized by two measures—expected return and risk. R. Brealy, An introduction to Risk and Return for Common Stocks (1969). It is axiomatic that risk and expected return are correlated: the higher the risk, the greater the expected return; the lower the risk, the smaller the expected return. J. Lorie and M. Hamilton, The Stock Market: Theories and Evidence (1973).
Efforts to obtain higher rates of return have focused on technical analysis and fundamental analysis. Technical analysis theorizes that buying and selling patterns in financial markets are random occurrences that largely depend on investor psychology, without any predictable connection between future and past stock market data. Fama, Efficient Capital Markets: A Review of Theory and Empirical Work, 25 J. Finance 383 (1970). Fundamental analysis theorizes that stock prices are correlated with corporate earnings, and predictability depends on the availability of information or interpretations of information about relevant data. Cohen, Zinbarg & Zeikel, Investment Analysis and Portfolio Management, 739 (1973). Technical analysts “study past prices” and “buy stock”, whereas fundamental analysts “study reports” and “buy companies”. Sunny J. Harris, Trading 102: Getting Down To Business (1998). Neither technical analysis nor fundamental analysis, however, has provided a favorable edge in the assessment of future value of financial assets.
Much of both technical analysis and fundamental analysis relies heavily upon the mathematical procedure known as “indexing”. Simply stated, indexing merely means collecting and analyzing financial information about a group of financial assets and deriving there from quantitative measures that are thought to be useful in assessing value. Widely known and used daily indices include (1) the Dow Jones Industrial Average, which is calculated from about 30 “Blue Chip” stocks, (2) the Standard & Poors 500 Index, which is calculated from 500 stocks, (3) the AMEX Market Value Index, which tracks the average of stocks traded on the American Stock Exchange, and (4) the NASDAQ Composite Index, which tracks all of the stocks traded on the National Association Of Security Dealers exchange. The problem is that most indices are based upon historical assumptions and/or rules that cannot be guaranteed to apply realistically at any particular time.
As will be described in more detail below, the present invention relies upon measures that are more properly considered to be benchmarks than indices. The terms index and benchmark often are used somewhat interchangeably. However, strictly speaking a benchmark is commonly more of a reference within a localized process, while an index is more commonly viewed as a generally applicable statistical term. Webster's defines benchmark as “a standard or reference by which others can be measured or judged”, and index as “a number derived from a series of observations and used as an indicator or measure”. Statistics textbooks more specifically define an “index number” as “a single figure that shows how a whole set of related variables has changed over time or differs from place to place”. The present description uses benchmark in its more restricted sense to refer to a measure in the relatively restricted context of the present invention.
Portfolio/Fund Level Data
A critical element in the program of the present invention is publicly available portfolio data. There are at least two portfolio level fields of data (portfolio content and portfolio date) and two security level fields of data (CUSIPs or some other unique identifier and the number of shares for equities or par amounts for bonds). A CUSIP is a unique identifier. This data is generated from one or more of the following sources: Securities and Exchange Commission (“SEC”) filings (these are referred to as “EDGAR filings”) or the equivalent filings in other countries (i.e., in the case of those funds not registered in the United States). In the United States, all publicly traded funds are required to file at least semi-annual statements (i.e., one annual and one mid-year statement). Publicly traded funds issue annual, semi-annual and/or quarterly statements that provide a dated detailed list of securities comprising each portfolio/fund. Many mutual funds complexes, insurance companies, banks, etc. give detailed lists of the contents of their portfolios to various data providers. There are several data providers that compile security level data listings from both publicly and privately held portfolios/funds. Essentially these data providers use various combinations of the above sources to compile these listings.
Asset Class Data
Depending on the benchmark being constructed, certain fields are matched with portfolio data. For example, certain equity portfolio data will require a description of the security, sector code (possibly based on the Standard Industrial Classification (SIC) code), etc. A high yield corporate bond portfolio might additionally require coupon, maturity, call schedule, etc. This general set of data is designed to completely encompass the portfolio data and is referred to as the Asset Class Data. Depending on the asset class(es) the securities are drawn from, there are typically several firms that provide this type of data to those firms that manage the portfolios being benchmarked. Several brokerage firms (e.g., Merrill Lynch and Salomon/Smith Barney) as well as several firms unrelated to the brokerage and financial management industry provide this information (e.g., J. J. Kenny, which is owned by Standard and Poors, or EJV/Bridge).
Portfolio Tracking Data
Related to the Portfolio Data is the Portfolio Tracking Data. These values are used to aid in tracking those portfolios that are used to construct the benchmarks and used to determine expenses charged to shareholders. This data is currently available from the following two primary sources: (1) Lipper provides portfolio level data (e.g., Net Asset Values (“NAVs”), returns, distribution yields, management fees, total expenses, defined asset groupings, etc.) for all publicly traded open-end funds, closed-end funds, annuity/insurance products, etc. Of particular importance are the NAVs and financial performance data. (2) Morningstar provides portfolio level data (e.g., Morningstar 3 year, 5 year, and 10 year ratings, management fees, total expenses, as well as defined asset groupings), which in many cases closely mimic those of Lipper.
Mutual Fund Performance
Studies of current mutual fund performance suggest the following: (1) Investors chase returns, namely, the summation of dividend distributions and capital appreciation. (2) Some fund returns can be slightly predictable. That is, past winners tend to continue to win and past losers tend to continue to lose. (3) The persistence in these funds is due almost exclusively to momentum stocks. In other words any persistent fund performance is due to holding stocks, not trading them in and out, as one would expect an “active” manager to do. Therefore, the appearance of superior “active” management is due to a basic buy and hold strategy not active trading. (4) There appears to be less persistent skill in the mutual fund industry than one would expect. In short, the mutual fund industry's record often is not impressive. (5) Therefore, the costly professionals hired by mutual fund firms often are not warranted. See: “Cochrane, John H., New Facts in Finance”, NBER Working Paper No. 7169, June 1999. P. 1–42.